Non-Stationary Stochastic Processes Estimation : : Vector Stationary Increments, Periodically Stationary Multi-Seasonal Increments / / Maksym Luz, Mikhail Moklyachuk.
The problem of forecasting future values of economic and physical processes, the problem of restoring lost information, cleaning signals or other data observations from noise, is magnified in an information-laden word. Methods of stochastic processes estimation depend on two main factors. The first...
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| Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2024] ©2024 |
| Year of Publication: | 2024 |
| Language: | English |
| Series: | De Gruyter Textbook
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| Online Access: | |
| Physical Description: | 1 online resource (XVIII, 292 p.) |
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Table of Contents:
- Frontmatter
- Introduction
- Contents
- Notations and abbreviations
- 1 Periodically stationary multi-seasonal increments of stochastic sequences
- 2 Extrapolation of sequences with periodically stationary increments
- 3 Extrapolation of sequences with periodically stationary increments observed with noise
- 4 Interpolation of sequences with periodically stationary increments observed with or without noise
- 5 Filtering of sequences with periodically stationary increments
- 6 Continuous time stochastic processes with periodically correlated increments
- 7 Extrapolation of processes with periodically correlated increments
- 8 Extrapolation of processes with periodically correlated increments observed with noise
- 9 Interpolation of processes with periodically correlated increments observed with or without noise
- 10 Filtering of processes with periodically correlated increments
- 11 Filtering problem when signal and noise have periodically correlated increments
- Problems
- A Some models of non-stationary time series
- Bibliography
- Index